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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28084

Title: Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation
Authors: Dah-Jing Jwo;Sheng-Hung Wang
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: Adaptive extended Kalman filtering;fuzzy logic adaptive system (FLAS);global positioning system (GPS);strong tracking Kalman filter (STKF)
Date: 2007-05
Issue Date: 2011-10-21T02:35:50Z
Publisher: IEEE Sensors Journal
Abstract: abstract:The well-known extended Kalman filter (EKF) has been widely applied to the Global Positioning System (GPS) navigation processing. The adaptive algorithm has been one of the approaches to prevent the divergence problem of the EKF when precise knowledge on the system models are not available. One of the adaptive methods is called the strong tracking Kalman filter (STKF), which is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. Traditional approach for selecting the softening factors heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is carried out. In the AFSTKF, the fuzzy logic reasoning system based on the Takagi–Sugeno (T-S) model is incorporated into the STKF. By monitoring the degree of divergence (DOD) arameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the softening factor according to the change in vehicle dynamics. GPS navigation processing using the AFSTKF will be simulated to validate the effectiveness of the proposed strategy. The performance of the proposed scheme will be assessed and compared with those of conventional
Relation: 7(5), pp.778-789
URI: http://ntour.ntou.edu.tw/handle/987654321/28084
Appears in Collections:[通訊與導航工程學系] 期刊論文

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